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Admission Control Algorithms for Revenue Optimization with QoS Guarantees in Mobile Wireless Networks

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Title: Admission Control Algorithms for Revenue Optimization with QoS Guarantees in Mobile Wireless Networks


1
Admission Control Algorithms for Revenue
Optimization with QoS Guarantees in Mobile
Wireless Networks
  • Authors I.R. Chen, O. Yilmaz and I.L. Yen
  • Presented By Rose Njeck, David Jones,
  • Kenneth Nehring

2
Agenda
  • Introduction
  • System Model
  • Mobility Service Call Pattern
  • Admission Control for Revenue Optimization with
    QoS Guarantees
  • Partitioning Admission Control
  • Threshold-Based Admission Control
  • Hybrid Partitioning and Threshold-Based Admission
    Control
  • Numeric Data Analysis
  • Applicability and Summary

3
Introduction
  • Next generation wireless networks
  • Real-time multimedia video and audio
  • Non-real-time services images and files
  • Requires network that easily adapts to
  • User needs
  • Growing population
  • Without compromising Quality of Service (QoS)

Two of the most important QoS measures in
cellular networks percentage of new and handoff
calls blocked due to channel unavailability
4
Introduction
Establish communication with base station
Connection may be dropped during handoff
Base station supports limited number of
connections
Handoff occurs when mobile user with ongoing
connection enters new cell
Reduce handoff call drop probability by rejecting
new connection requests, but results in increase
of new call blocking probability
Tradeoff between handoff and new call blocking
probabilities
5
Introduction
  • Partition, threshold-based and hybrid admission
    control algorithms make acceptance/rejection
    decisions based on
  • Satisfying QoS requirements
  • Optimizing revenue
  • Achieved by integrating pricing with call
    admission control
  • Assume static charge-by-time pricing algorithm

6
Introduction
  • Definitions
  • Partitioning number of channels are reserved to
    serve handoff (or new) calls of a service type
  • Threshold-based handoff (or new) calls of a
    service type are given an admission threshold

7
System Model
  • Cellular Network as flat architecture cells
    connected consecutively
  • Base station at the center of each cells that
    provides services to mobile hosts within the cell
  • Distinct number of service classes characterized
    by their type attribute
  • Real-time services
  • Non real-time services

8
System Model
  • Each service type has
  • Handoff calls with higher priority
  • New calls
  • Might impose system wide QoS requirement
  • Each service class i has a QoS constraint on Biht
    and on Bint

9
System Model- single cell perspective
  • Service class characteristics

lin Arrival rate of new calls of service class
i min Departure rate of new calls of service
class i lih Arrival rate of handoff calls of
service class i mih Departure rate of handoff
calls of service class i
  • Cell has C channel
  • C depends on the available bandwidth
  • Service call of class i requires ki channels

10
System Model service provider perspective
  • each cell makes admission control decisions for
    new and handoff call requests taking into
    consideration of the price rate information of
    these service calls in order to maximize the
    revenue received from servicing new and handoff
    calls in the cell.
  • Price-rate scheme adopted
  • Calls of service class i have a charge rate of vi
    per time unit

11
Mobility Service Call Patterns
  • Mobility and service call patterns are used by
    cells in a wireless network to make admission
    control decisions to allocate resources to calls.
  • Requires each mobile user to intelligently know
    their expected arrival and departure rate for the
    current cell they occupy.
  • Need a mechanism to estimate these rates.

Need a way to estimate lin Arrival rate of
new calls of service class i min Departure
rate of new calls of service class i lih
Arrival rate of handoff calls of service class
i mih Departure rate of handoff calls of
service class i
lin
min
lih
min
12
Mobility Service Call Patterns
  • Mobility service call pattern recognition
    algorithm executed on individual mobile devices
  • Helps to achieve scalability
  • Two data structures stored on mobile devices to
    summarize the data computed from the algorithm
  • Mobility Probability Matrix
  • Service Call Table

13
Mobility Probability Matrix
  • Summarizes the probability (PBCD) of the mobile
    user going from one cell to the next cell and the
    residence time (TBCD) of each cell, given that
    the mobile user comes from a previous cell.

PBCD
B
C
D
TBCD
You are here
14
Mobility Probability Matrix
  • To calculate probabilities, reward correct state
    transitions and penalize incorrect ones.
  • The sum of all probabilities is one.

C can go to D1 through D6
D1
D6
D2
With a transition to D3, PBCD3 is rewarded 10
while the rest are penalized 2
C
D5
D3
D4
15
Mobility Probability Matrix
  • For mobile users that exhibits a certain degree
    of regularity for movements and calls, the matrix
    will eventually concentrate on certain state
    transition probabilities with values close to 1.
  • The matrix will summarize the regular paths taken
    by the mobile user.

Average Dwell Time
  • TBCD1, TBCD2, TBCD3, TBCD4, TBCD5, and TBCD6 are
    updated accordingly depending on the actual path
    taken by the mobile user.
  • Values are easily determined by keeping track of
    the average dwell time that the mobile user stays
    in a particular cell, given the history of the
    previous cell and the next cell.

16
Service Call Table
  • Maintained by individual mobile devices to
    summarize call patterns.
  • Populated as the mobile device goes through a
    sequence of calls.
  • Stores four rate values for each cell visited
    by a mobile device.

Rate Values Stored Ln(C) Arrival rate of a
new call made in cell C qn(C) Departure rate
of a new call from cell C Lh(C) Arrival rate
of a handoff call from cell C into
its neighbor cells qh(C) Departure rate of
a terminated handoff call from cell C
qn(C)
C
Ln(C)
qn(C)
Lh(C)
17
Arrival Departure Rates for a Cell
Arrival rate of handoff calls for cell C
Lh(B) Arrival rate of a handoff call from cell
B into its neighbors
M set of neighbor cells of cell C
18
Arrival Departure Rates for a Cell
Arrival rate of new calls for cell C
Departure rate of new calls for cell C
Note that the arrival rate of all new calls is an
aggregate measure summing all new call arrival
rates by individual users in the cell, while the
departure rate per call is an average parameter,
averaging over all the mobile users in the cell.
Departure rate of handoff calls for cell C
19
Admission Control for Revenue Optimization with
QoS Guarantes
  • Partitioning Admission Control
  • Threshold-Based Admission Control
  • Hybrid Admission Control
  • Assume two service types
  • class 1 (high-priority)
  • l1n, m1n, l1h, and m1h
  • class 2 (low-priority)
  • l2n, m2n, l2h, and m2h

20
Partitioning Admission Control
A partitioning call admission control policy
divides the total number of channels in a cell
into several fixed partitions with each partition
specifically reserved to serve a particular
service class (real-time vs. non-real-time) and
call type (new vs. handoff).
C1h, C1n, C2h, C2n C ltltConstraintsgtgt C1h
C1n C2h C2n C
21
Partitioning Admission Control
  • Channels in a partition cannot be shared.
  • If a new high-priority (i.e. class 1) call
    arrives at a cell and all channels allocated to
    serve high-priority new calls are used up, the
    call is rejected. This applies to all service
    classes and call types.
  • Like a M/M/n1n/n1n queue where n1n number of
    call slots, with arrival rate l1n and service
    rate m1n.

22
Input Parameters to a Cell
The following parameters are used by a cells
admission control algorithm
Arrival Rates
Departure Rates
Number of Channels
Price Rates
l1h l1n l2h l2n
m1h m1n m2h m2n
v1 v2
k1 k2
Threshold Blocking Probabilities
B1ht B1nt B2ht B2nt
23
QoS Constraints
  • A blocking probability is the probability a call
    is rejected.
  • Blocking probabilities of new and handoff calls
    for both classes 1 and 2 must be satisfied.
  • We would like to partition the channels such that
    the following QoS constraints are satisfied

B1h lt B1ht B1n lt B1nt B2h lt B2ht B2n lt B2nt
The blocking probabilities can be easily
determined by calculating the probability of the
partition allocated to serve the specific calls
being full.
24
Revenue Generation
The revenue that a successfully terminated or
handed-off call brings to the cell is calculated
by the product of the calls price rate parameter
vi with the duration of the call in the
cell. Under partitioning, a cell will receive
the following revenue per time unit
N1h Number of high-priority handoff
.
calls in the cell N1n Number of
high-priority new calls
. in the
cell N2h Number of low-priority handoff

. calls in the cell N1n Number of
low-priority new calls
. in the cell
25
Revenue Generation
The revenue rate earned by the partitioning
algorithm is as follows
PR(C, l1h, l1n, l2h, l2n) PR1h PR1n PR2h
PR2n
PR1h, PR1n, PR2h, and PR2n stand for the revenues
generated per unit time due to high-priority
handoff calls, high-priority new calls,
low-priority handoff calls, and low-priority new
calls respectively.
26
Revenue Optimization
  • Need to identify the best partition sizes (C1h,
    C1n, C2h, C2n) that will maximize the cells
    revenue subject to the imposed QoS constraints.

27
Threshold-Based Admission Control
C1h gt CT , C1n gt CT ltltConstraintsgtgt
C2h CT ,C2n CT
28
Threshold-Based Admission Control
  • SPN Model

29
Threshold-Based Admission Control
  • SPN Model

Eih models handoff call arrivals of service
class i at rate ?ih Ein models new call
arrivals of service class i at rate ?in Sih
models service of handoff call arrivals of
service class i with a service rate of M(UCih)
multiplied with µih where M(UCih) stands for the
number of tokens in place UCih Sin models
service of new call arrivals of service class i
with a service rate of M(UCin) multiplied with
µin where M(UCin) stands for the number of tokens
in place UCin UCin models the execution state
of service class i new calls UCih models the
execution state of service class i handoff calls
30
Threshold-Based Admission Control
  • A new service request arrival is admitted only if
    the threshold assigned is not yet reached.
  • ? Assign an enabling predicate to guard Ein, Eih
    with thresholds Cin and Cih

31
Threshold-Based Admission Control
  • Enabling predicate of E1n
  • M(UC1n) M(UC1h) k1 k1 M(UC2n) M(UC2h)
    k2 C1n
  • Enabling predicate of E1h is
  • M(UC1n) M(UC1h) k1 k1 M(UC2n)
    M(UC2h) k2 C1h
  • Enabling predicate of E2n is
  • M(UC1n) M(UC1h) k1 k2 M(UC2n)
    M(UC2h) k2 C2n
  • Enabling predicate of E2h is
  • M(UC1n) M(UC1h) k1 k2 M(UC2n) M(UC2h)
    k2 C2h

32
Threshold-Based Admission Control
  • SPN Model- Blocking probabilities
  • where rate(Eic) is calculated by finding the
    expected value of a random variable X defined as
    X ?ic if Eic is enabled and 0 otherwise

33
Revenue Generation
The revenue generated per unit time from the
threshold-based admission control algorithm to
the cell is defined by
Where TR1h, TR1n, TR2h, and TR2n stand for the
revenues generated per unit time due to
high-priority handoff calls, high-priority new
calls, low-priority handoff calls, and
low-priority new calls, respectively, given by
34
Hybrid Admission Control
The hybrid algorithm divides the channels into
fixed partitions the same way partitioning
algorithm does. In addition, a shared partition
is reserved to allow calls of all service classes
to compete for usage in accordance to threshold
algorithm.
n1hsk1 n1nsk1 n2hsk2 n2nsk2 Cs
Constraints C1h C1n C2h C2n Cs C
35
Hybrid Admission Control
  • The shared partition is available for use by a
    service class only if the partition reserved for
    that service class is used up.
  • QoS constraints and revenue earned per unit time
    remain applicable

B1h lt B1ht B1n lt B1nt B2h lt B2ht B2n lt B2nt
36
Hybrid Performance Model
  • Hybrid algorithm encompasses algorithms as
    special cases
  • Partitioning Cs0
  • Threshold-based C1h, C1n, C2h, C2n all equal 0
  • Hybrid performance model composed of two
    submodels
  • Partitioning C1h, C1n, C2h, C2n
  • Threshold-based CCs

37
Hybrid Performance Model
  • Shared partition arrival rates
  • Arrival rate is the sum of spill over rates from
    each fixed partition (modeled as M/M/n/n queues)

Arrival rates into shared partition ? 1hs high
priority handoff calls ?1ns high priority new
calls ?2hs low priority handoff calls ?2 ns
low priority new calls
Erlangs B formula
Similar expressions for ?1ns, ?2hs, and ?2ns
38
Hybrid Revenue Generation
  • Revenue generated per unit time from hybrid
    admission control algorithm is sum of revenues
    earned from the fixed partitions plus that earned
    from the shared partition

HR(C, ?1h, ?1n, ?2h, ?2n) PR(C-Cs, ?1h, ?1n,
?2h, ?2n) TR(Cs, ?1hs, ?1ns, ?2hs, ?2ns)
Optimization for hybrid admission control
algorithm Identify the best partition (C1h, C1n,
C2h, C2n, Cs) to maximize the revenue subject to
imposed QoS constraints
39
Numeric Data and Analysis
  • Results include partitioning, threshold-based and
    hybrid admission control algorithms for revenue
    optimization with QoS guarantees
  • Charging rate model based on popular
    charge-by-time scheme
  • Two classes of service
  • Class 1 (real-time) demands more resources and
    higher QoS
  • Class 2 (non-real-time)

40
Algorithm Comparisons, ?1h
?1h Partitioning Partitioning Hybrid Hybrid Threshold-based Threshold-based
?1h (C1h, C1n, C2h, C2n) Revenue/Time (C1h,C1n,C2h, C2n,Cs) Revenue/Time (C1hT,C1nT, C2hT, C2nT ) Revenue/Time
1 (16,56,4,4) 577.391 (8, 72,0,0,36) 580.000 (80,80,80,80) 579.95
1.5 (20,52,4,4) 615.486 (12,36,0,0,32) 620.000 (80,80,80,80) 619.88
2 (20,52,4,4) 652.304 (12,32,0,0,36) 659.997 (80,80,80,80) 659.75
2.5 (28,44,4,4) 686.660 (16,32,0,0,32) 699.986 (80,80,80,80) 699.485
3 (32,40,4,4) 717.032 (16,32,0,0,32) 739.949 (80,80,80,80) 739.023
3.5 (32,40,4,4) 754.215 (16,28,0,0,36) 779.842 (80,80,76,76) 778.258
4 None None (16,28,0,0,36) 819.565 (80,80,76,76) 817.058
4.5 None None (20,24,0,0,36) 858.998 (80,80,76,76) 855.266
5 None None (20,24,0,0,36) 897.974 (80,80,76,76) 892.708
5.5 None None (20,24,0,0,36) 936.137 (80,80,76,76) 929.203
6 None None (20,20,0,0,40) 973.303 (80,80,76,76) 964.569
6.5 None None (20,20,0,0,40) 1009.098 (80,76,75,72) 992.917
7 None None (24,20,0,0,36) 1043.262 None None
7.5 None None (24,20,0,0,36) 1075.786 None None
C80, ?1h 1.0, ?1n 6.0, ?1n 1.0, ?2h 1.0, ?2h 1.0, ?2n 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 6.0, ?1n 1.0, ?2h 1.0, ?2h 1.0, ?2n 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 6.0, ?1n 1.0, ?2h 1.0, ?2h 1.0, ?2n 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 6.0, ?1n 1.0, ?2h 1.0, ?2h 1.0, ?2n 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 6.0, ?1n 1.0, ?2h 1.0, ?2h 1.0, ?2n 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 6.0, ?1n 1.0, ?2h 1.0, ?2h 1.0, ?2n 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 6.0, ?1n 1.0, ?2h 1.0, ?2h 1.0, ?2n 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1.
An increase in revenue/time equals
41
Analysis, ?1h
  • As ?1h increases, the revenue rate obtainable
    also increases as long as QoS constraints can
    still be satisfied given the amount of resources
    available
  • As ?1h increases further past a threshold value,
    all algorithms eventually fail to yield a
    solution because workload is too heavy to satisfy
    the imposed QoS constraints
  • The hybrid admission control is the most tolerant
    among all in terms of being able to yield a
    solution under high workload situations

42
Analysis, ?1h
  • Superiority of hybrid admission control over
    partitioning and threshold-based admission
    control due to
  • Ability to optimally reserve dedicated resources
    for high-priority classes through fixed
    partitioning to reduce interference from
    low-priority classes
  • Ability to optimally allocate resources to the
    shared partition in accordance with
    threshold-based admission control to exploit the
    multiplexing power for all classes

43
Algorithm Comparisons, ?2h ?2n
?2h?2n Partitioning Partitioning Hybrid Hybrid Threshold-based Threshold-based
?2h?2n (C1h, C1n, C2h, C2n) Revenue/Time (C1h,C1n,C2h, C2n,Cs) Revenue/Time (C1hT,C1nT, C2hT, C2nT ) Revenue/Time
1 (48,24,4,4) 576.382 (32,16,0,0,32) 580.000 (80,80,80,80) 579.952
2 (44,24,6,6) 594.268 (28,12,0,0,40) 599.999 (80,80,80,80) 599.917
3 (44,20,8,8) 610.326 (28,12,0,0,40) 619.998 (80,80,80,80) 619.855
4 (44,20,8,8) 628.380 (28,12,1,1,38) 639.993 (80,80,80,80) 639.755
5 (40,20,10,10) 644.636 (28,12,2,2,36) 659.977 (80,80,80,80) 659.593
6 (40,20,11,9) 660.886 (24,8,2,2,44) 679.937 (80,80,80,80) 679.338
7 None None (24,8,2,2,44) 699.854 (80,80,80,80) 698.948
8 None None (24,8,3,3,42) 719.675 (80,80,80,80) 718.365
9 None None (20,8,3,3,46) 739.321 (80,80,76,76) 737.525
10 None None (20,8,3,3,46) 758.708 (80,80,76,76) 756.341
11 None None (20,8,4,4,44) 777.650 (80,80,76,76) 774.714
12 None None (20,4,3,3,50) 795.995 (80,80,76,76) 792.533
13 None None (16,4,3,3,54) 813.654 (80,80,76,76) 809.685
14 None None (16,4,3,3,54) 830.339 (80,80,76,76) 826.054
15 None None (16,4,3,3,54) 845.795 (80,80,76,76) 841.533
16 None None (16,4,6,6,48) 859.543 (80,80,76,75) 855.576
17 None None (12,0,2,2,64) 872.773 None None
C80, ?1h 5.0, ?1h 1.0, ?1n 2.0, ?1n 1.0, ?2h 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 5.0, ?1h 1.0, ?1n 2.0, ?1n 1.0, ?2h 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 5.0, ?1h 1.0, ?1n 2.0, ?1n 1.0, ?2h 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 5.0, ?1h 1.0, ?1n 2.0, ?1n 1.0, ?2h 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 5.0, ?1h 1.0, ?1n 2.0, ?1n 1.0, ?2h 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 5.0, ?1h 1.0, ?1n 2.0, ?1n 1.0, ?2h 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1. C80, ?1h 5.0, ?1h 1.0, ?1n 2.0, ?1n 1.0, ?2h 1.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B1nt 0.05, B2nt 0.1.
44
Analysis, ?2h ?2n
  • As the arrival rate of low-priority class
    increases, hybrid admission control
  • Decreases the number of dedicated channels
    allocated to high-priority calls
  • Increases the number of shared channels to
    exploit the multiplexing power in the shared
    partition
  • Attempts to allocate as much resources to
    low-priority calls as possible since the system
    will gain most of its revenue from low-priority
    calls
  • Hybrid admission control performs the best over a
    wide range of arrival rate of low-priority calls

45
Algorithm Comparisons, v1v2 (v210)
v1 v2 Partitioning Partitioning Hybrid Hybrid Threshold-based Threshold-based
v1 v2 (C1h, C1n, C2h, C2n) Revenue/Time (C1h,C1n,C2h, C2n,Cs) Revenue/Time (C1hT,C1nT, C2hT, C2nT ) Revenue/Time
Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0)
1 (20, 16, 22, 22) 219.735 (12,12,5,5,46) 220.000 (80,80,80,80) 220.000
2 (20, 16, 22, 22) 239.550 (12,12,5,5,46) 240.000 (80,80,80,80) 240.000
4 (20, 20, 20, 20) 279.381 (16,12,5,5,42) 280.000 (80,80,80,80) 280.000
8 (20, 20, 20, 20) 359.135 (16,12,5,5,42) 360.000 (80,80,80,80) 360.000
16 (20, 20, 20, 20) 518.645 (16,16,7,5,36) 520.000 (80,80,80,80) 520.000
32 (20, 20, 20, 20) 837.663 (16,16,7,5,36) 840.000 (80,80,76,76) 840.000
64 (24, 20, 18, 18) 1476.281 (16,16,7,5,36) 1480.000 (80,80,76,76) 1480.000
128 (24, 24, 16, 16) 2754.232 (16,16,7,5,36) 2760.000 (80,80,76,76) 2760.000
High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5)
1 None None (8,12,5,5,50) 278.919 (80,80,80,80) 278.280
2 None None (12,16,4,4,44) 358.240 (80,80,80,80) 357.129
4 None None (12,16,3,3,46) 516.987 (80,80,80,80) 514.828
8 None None (12,16,2,2,48) 834.545 (80,80,76,76) 830.611
16 None None (12,16,1,1,50) 1469.720 (80,80,72,72) 1464.843
32 None None (12,16,0,0,52) 2747.443 (80,80,72,69) 2736.794
64 None None (12,16,0,0,52) 5303.173 (80,80,71,66) 5284.153
128 None None (12,16,0,0,52) 10416.435 (80,80,71,66) 10380.503
C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B2nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B2nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B2nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B2nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B2nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B2nt 0.05, B2nt 0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v2 10, k1 4, k2 1, B1ht 0.02, B2ht 0.04, B2nt 0.05, B2nt 0.1.
46
Analysis, v1v2 (v210)
  • Difference in revenue earned becomes more
    significant as the v1v2 ratio increases
  • Especially pronounced when system is heavily
    loaded under which it is necessary to optimally
    allocate channels to calls to maximize revenue
    and satisfy imposed QoS constraints
  • Hybrid admission control either outperforms or is
    as good as partitioning and threshold-based
    admission control

47
Algorithm Comparisons, QoS
(B1ht, B2ht) Partitioning Partitioning Hybrid Hybrid Threshold-based Threshold-based
(B1ht, B2ht) (C1h, C1n, C2h, C2n) Revenue/ Time (C1h,C1n,C2h, C2n,Cs) Revenue/ Time (C1hT,C1nT, C2hT, C2nT ) Revenue/ Time
Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0) Low Class 1 Call Arrival Rates (?1h 1.0, ?1n 1.0)
(0.02,0.04) x 20 (20,20,20,20) 359.135 (16,12,5,5,42) 360.000 (80,80,80,80) 359.999
(0.02,0.04) x 2-1 (20,20,20,20) 359.135 (16,12,5,5,42) 360.000 (80,80,80,80) 359.999
(0.02,0.04) x 2-2 (20,20,20,20) 359.135 (16,12,5,5,42) 360.000 (80,80,80,80) 359.999
(0.02,0.04) x 2-3 (24,20,20,20) 358.345 (16,12,5,5,42) 360.000 (80,80,80,80) 359.999
(0.02,0.04) x 2-4 (24,20,20,20) 358.345 (16,12,5,5,42) 360.000 (80,80,80,80) 359.999
(0.02,0.04) x 2-5 (24,20,21,19) 358.264 (16,12,5,5,42) 360.000 (80,80,80,80) 359.999
(0.02,0.04) x 2-6 (28,16,22,14) 353.041 (16,12,5,5,42) 360.000 (80,80,80,80) 359.999
(0.02,0.04) x 2-7 (28,16,23,13) 350.311 (16,12,5,5,42) 360.000 (80,80,80,80) 359.999
None None
(0.02,0.04) x 2-21 None None (16,12,5,5,42) 360.000 (80,76,76,61) 359.991
(0.02,0.04) x 2-22 None None (16,12,5,5,42) 360.000 (80,76,76,54) 359.904
(0.02,0.04) x 2-23 None None (16,12,5,5,42) 360.000 (80,76,76,48) 359.409
(0.02,0.04) x 2-24 None None (16,12,5,5,42) 360.000 (80,76,76,42) 357.231
(0.02,0.04) x 2-25 None None (16,12,5,5,42) 360.000 None None
High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5) High Class 1 Call Arrival Rates (?1h 3.5, ?1n 4.5)
(0.02,0.04) x 20 None None (12,16,2,2,48) 834.544 (80,80,76,76) 830.610
(0.02,0.04) x 2-1 None None (12,16,2,2,48) 834.544 (80,80,76,76) 830.610
(0.02,0.04) x 2-2 None None (20,8,1,1,50) 830.078 (80,76,76,76) 826.208
C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1nt0.05, B2nt0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1nt0.05, B2nt0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1nt0.05, B2nt0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1nt0.05, B2nt0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1nt0.05, B2nt0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1nt0.05, B2nt0.1. C80, ?1h 1.0, ?1n 1.0, ?2h 10.0, ?2h 1.0, ?2n 10.0, ?2n 1.0, v1 80, v2 10, k1 4, k2 1, B1nt0.05, B2nt0.1.
48
Analysis, QoS
  • Under light-load conditions, all three algorithms
    can reasonably adapt to the QoS change
  • Partitioning admission control generates
    relatively lower revenue because without
    multiplexing power, it needs to trade revenue off
    for QoS
  • When QoS constraints of handoff calls becomes
    extremely tight, both partitioning and
    threshold-based admission control algorithms fail
    to provide solutions
  • Hybrid admission is able to provide a solution
    due to its ability to exploit the multiplexing
    power in the shared partition and to reserve
    dedicated resources for individual service classes

49
Analysis, QoS
  • Under heavy-load situations, hybrid admission
    control is more adaptable to stringent QoS
    constraints
  • Hybrid admission control allocates more channels
    in
  • The C1h partition and conversely fewer channels
    in C1n to satisfy the most stringent QoS
    constraint imposed on class 1 handoff calls
  • The shared partition to satisfy stringent QoS
    requirement of class 2 handoff calls, which
    through multiplexing also has the benefit of
    compensating class 1 and class 2 new calls to
    satisfy QoS constraints
  • The channel allocation made by hybrid admission
    control algorithm represents best possible way to
    satisfy varying QoS requirements while maximizing
    revenue earned

50
Summary
  • Analyzed design concept for the integration of
    pricing with admission control algorithms with
    QoS guarantees in a cellular wireless network
  • Admission control algorithm should consider not
    only QoS constraints imposed by system, but also
    the revenue that the admission of such a call
    will bring to the system when deciding which
    calls to admit
  • Three admission control algorithms with intention
    of maximizing revenue generated by a cell while
    satisfying QoS constraints
  • Partitioning
  • Threshold-based
  • Hybrid admission control

51
Summary
  • Using optimized conditions, the hybrid admission
    control algorithm can generate higher revenue
    with QoS guarantee than the other two admission
    control algorithms
  • Attribute the superiority of the hybrid algorithm
    to
  • Existence of fixed partitions reserved for
    specific classes to avoid interference from other
    classes so as to satisfy the QoS requirements
  • Shared partition which provides great
    multiplexing power for sharing the bandwidth
    among calls of different classes

52
Applicability
  • Cell dynamically communicates with mobile users
    in its cell and neighboring cells to obtain
    values or arrival and departure rates of
    new/handoff calls of various service classes
  • Performs a simple table lookup at runtime to
    obtain the optimal (Ch1, Cn1, Ch2, Cn2, Cs)

53
Future research
  • Consider other pricing models and investigate
    optimal resource allocation settings
  • Consider other revenue collection models
  • Revenue only collected on call termination
  • Revenue is lost when call terminated prematurely
  • Explore relationship between QoS and pricing
  • Determine the optimal pricing for calls of
    various service classes
  • Such that revenue is maximized with QoS
    guarantees based on anticipated workload
    conditions and resource availability

54
Questions?
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